equation, H‐infinity control, H‐infinity Controller design via DGKF and LMI techniques, H‐infinity
loop shaping technique, Structured singular value (mu) synthesis, Design examples.
Text/Reference Books:
1. D.S.Naidu, Optimal Control Systems, CRC Press
2. Sinha, Linear Systems Optimal and Robust Control, CRC Press
3. D.E.Kirk, Optimal Control Theory An Introduction, PHI.
4. K.Morris, Introduction to Feedback Control, Academic Press.
5. Helton, Merino, Classical Control using H∞ Methods, 1/e, SIAM Publica ons
6. Ozbay, Introduction to Feedback Control Theory, CRC Press
7. Gu, Petkov, Konstantinov, Robust Control Design with MATLAB, Springer India
8. Qiu, Zhou, Introduction to Feedback Control, Prentice Hall, 2009.
AV466
ESTIMATION AND STOCHASTIC THEORY
(3‐ 0 ‐ 0) 3 credits
Elements of probability theory ‐ random variables‐Gaussian distribution‐stochastic processes‐
characterizations and properties‐Gauss‐Markov processes‐Brownian motion process‐Gauss‐
Markov models ‐ Optimal estimation for discrete‐time systems ‐ fundamental theorem of
estimation‐optimal prediction.
Optimal filtering ‐ Weiner approach‐continuous time Kalman Filter‐properties and
implementation‐steady‐state Kalman Filter‐discrete‐time Kalman Filter‐implementation‐sub‐
optimal steady‐state Kalman Filter‐Extended Kalman Filter‐practical applications.
Optimal smoothing ‐ 0ptimal fixed‐interval smoothing optimal fixed‐point smoothing‐optimal
fixed‐lag smoothing‐stability‐performance evaluation.
Text/Reference books:
1. M.D. Srinath, P.K. Rajasekaran and R. Viswanathan: Statistical Signal Processing with
Applications, PHI, 1996.
2. D.G. Manolakis, V.K. Ingle and S.M. Kogon: Statistical and Adaptive Signal
Processing, McGraw Hill, 2000.
3. S. M. Kay: Modern Spectral Estimation, Prentice Hall, 1987.
4. H. V. Poor, "An Introduction to Signal Detection and Estimation", Springer, 2/e,
1998.
5. S. M. Kay, "Fundamentals of Statistical Signal Processing: Estimation Theory",
Prentice Hall PTR, 1993.
6. M.S. Grewal, A.P. Andrews, “Kalman filtering : Theory and Practice”, Second edition,
John Wiley & Sons, 2001.
7. C.K. Chui, G. Chen, “Kalman Filtering with Real‐Time Applications”, Third edition,
Springer‐Verlag,1999.
8. R.G. Brown, Y.C. Hwang, “Introduction to Random Signals and Applied Kalman
Filtering”, Second edition, John Wiley & Sons, 1992.
AV467
INTRODUCTION TO OPTIMIZATION AND OR
(3‐ 0 ‐ 0) 3 credits
Vector spaces and matrices, transformations, eigenvalues and eigenvectors, norms; geometrical
concepts ‐‐ hyperplanes, convex sets, polytopes and polyhedra; unconstrained optimization ‐‐
condition for local minima; one dimensional search methods ‐‐ golden section, fibonacci,
newtons, secant search methods; gradient methods ‐‐ steepest descent; newton's method,
conjugate direction methods, conjugate gradient method; constrained optimization ‐‐ equality
conditions, lagrange condition, second order conditions; inequality constraints ‐‐ karush‐kuhn‐
tucker condition; convex optimization; introduction to assignment problem, decision analysis,
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